Mercurial Hosting > traffic-intelligence
view python/cvutils.py @ 116:2bf5b76320c0
moved intersection plotting and added markers for scatter plots
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Mon, 08 Aug 2011 14:47:30 -0400 |
parents | 67555e968b5e |
children | 0f552c8b1650 |
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#! /usr/bin/env python '''Image/Video utilities''' import Image, ImageDraw # PIL try: import cv opencvExists = True except ImportError: print('OpenCV library could not be loaded') opencvExists = False from sys import stdout #import aggdraw # agg on top of PIL (antialiased drawing) #import utils __metaclass__ = type def drawLines(filename, origins, destinations, w = 1, resultFilename='image.png'): '''Draws lines over the image ''' img = Image.open(filename) draw = ImageDraw.Draw(img) #draw = aggdraw.Draw(img) #pen = aggdraw.Pen("red", width) for p1, p2 in zip(origins, destinations): draw.line([p1.x, p1.y, p2.x, p2.y], width = w, fill = (256,0,0)) #draw.line([p1.x, p1.y, p2.x, p2.y], pen) del draw #out = utils.openCheck(resultFilename) img.save(resultFilename) def computeHomography(srcPoints, dstPoints, method=0, ransacReprojThreshold=0.0): '''Returns the homography matrix mapping from srcPoints to dstPoints (dimension Nx2)''' cvSrcPoints = arrayToCvMat(srcPoints); cvDstPoints = arrayToCvMat(dstPoints); H = cv.CreateMat(3, 3, cv.CV_64FC1) cv.FindHomography(cvSrcPoints, cvDstPoints, H, method, ransacReprojThreshold) return H def cvMatToArray(cvmat): '''Converts an OpenCV CvMat to numpy array.''' from numpy.core.multiarray import zeros a = zeros((cvmat.rows, cvmat.cols))#array([[0.0]*cvmat.width]*cvmat.height) for i in xrange(cvmat.rows): for j in xrange(cvmat.cols): a[i,j] = cvmat[i,j] return a if opencvExists: def arrayToCvMat(a, t = cv.CV_64FC1): '''Converts a numpy array to an OpenCV CvMat, with default type CV_64FC1.''' cvmat = cv.CreateMat(a.shape[0], a.shape[1], t) for i in range(cvmat.rows): for j in range(cvmat.cols): cvmat[i,j] = a[i,j] return cvmat def printCvMat(cvmat, out = stdout): '''Prints the cvmat to out''' for i in xrange(cvmat.rows): for j in xrange(cvmat.cols): out.write('{0} '.format(cvmat[i,j])) out.write('\n') def projectArray(homography, points): '''Returns the coordinates of the projected points (format 2xN points) through homography''' from numpy.core._dotblas import dot from numpy.core.multiarray import array from numpy.lib.function_base import append if points.shape[0] != 2: raise Exception('points of dimension {0} {1}'.format(points.shape[0], points.shape[1])) if (homography!=None) and homography.size>0: augmentedPoints = append(points,[[1]*points.shape[1]], 0) prod = dot(homography, augmentedPoints) return prod[0:2]/prod[2] else: return p def project(homography, p): '''Returns the coordinates of the projection of the point p through homography''' from numpy.core.multiarray import array return projectArray(homography, array([[p[0]],p[1]])) def projectTrajectory(homography, trajectory): '''Projects a series of points in the format [[x1, x2, ...], [y1, y2, ...]]''' from numpy.core.multiarray import array return projectArray(homography, array(trajectory)) def invertHomography(homography): 'Returns an inverted homography' from numpy.linalg.linalg import inv invH = inv(homography) invH /= invH[2,2] return invH if opencvExists: def computeTranslation(img1, img2, img1Points, maxTranslation, minNMatches, windowSize = (5,5), level = 5, criteria = (cv.CV_TERMCRIT_EPS, 0, 0.01)): '''Computes the translation between of img2 with respect to img1 (loaded using OpenCV) img1Points are used to compute the translation TODO add diagnostic if data is all over the place, and it most likely is not a translation (eg zoom)''' from numpy.core.multiarray import zeros from numpy.lib.function_base import median (img2Points, status, track_error) = cv.CalcOpticalFlowPyrLK(img1, img2, zeros((img1.rows,img1.cols+8)), zeros((img1.rows,img1.cols+8)), img1Points, windowSize, level, criteria, 0) deltaX = [] deltaY = [] for (k, (p1,p2)) in enumerate(zip(img1Points, img2Points)): if status[k] == 1: dx = p2[0]-p1[0] dy = p2[1]-p1[1] d = dx**2 + dy**2 if d < maxTranslation: deltaX.append(dx) deltaY.append(dy) if len(deltaX) >= 10: return [median(deltaX), median(deltaY)] else: return None